Sharif Digital Repository

In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with random missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data. This paper will mainly focus on comparing the power of Iterative Method of Adaptive Thresholding (IMAT) in reconstruction of the desired sparse signal with that of LASSO. Additionally, we will assume the model has random missing information. Missing data has been recently of interest in big data and machine learning... 

In this paper, we introduce a novel and robust approach to quantized matrix completion. First, we propose a rank minimization problem with constraints induced by quantization bounds. Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss. Huber loss is leveraged to control the violation from quantization bounds due to two properties: first, it is differentiable; and second, it is less sensitive to outliers than the quadratic loss. A smooth rank approximation is utilized to endorse lower rank on the genuine data matrix. Thus, an unconstrained optimization problem with differentiable objective function is obtained allowing us to advantage from... 

In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations. However, only partial information about the matrix representing the linear combinations is available. Assuming a low-rank structure for the matrix, one natural solution would be to first apply a matrix completion to the data, and then to solve the resulting compressed sensing problem. In big data applications such as massive MIMO and medical data, the matrix completion step imposes a huge computational burden. Here, we propose to reduce the computational cost of the completion task by ignoring the columns corresponding to zero elements in the sparse vector. To... 

Adaptive thresholding methods have proved to yield a high signal-to-noise ratio (SNR) and fast convergence in sparse signal recovery. The robustness of a class of iterative sparse recovery algorithms, such as the iterative method with adaptive thresholding, has been found to outperform the state-of-art methods in respect of reconstruction quality, convergence speed, and sensitivity to noise. In this study, the authors introduce a new method for compressed sensing, using the sensing matrix and measurements. In our method, they iteratively threshold the signal and project the thresholded signal onto the translated null space of the sensing matrix. The threshold level is assigned adaptively.... 

Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired support of the signal and resisting against noisy systems. When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously. In this paper, we will introduce an approach which enjoys sparse regression and covariance matrix estimation to improve matrix completion accuracy, and as a result enhancing... 

In this paper, we propose two new algorithms for transductive multi-label learning from missing data. In transductive matrix completion (MC), the challenge is prediction while the data matrix is partially observed. The joint MC and prediction tasks are addressed simultaneously to enhance accuracy in comparison with separate tackling of each. In this setting, the labels to be predicted are modeled as missing entries inside a stacked matrix along the feature-instance data. Assuming the data matrix is of low rank, we propose a new recommendation method for transductive MC by posing the problem as a minimization of the smoothed rank function with non-affine constraints, rather than its convex... 

In spite of the extensive literature on the analysis of the muscle synergies during gait, the functionality of these synergies has not been studied in detail. This study explored the relationship between the motor modules and the kinematic maneuvers involved in human walking. Motion and surface electromyography data (of 28 trunk and lower extremity muscles) were acquired from ten healthy subjects during ten trials of self-selected speed gait each. The joint angle trajectories were half-wave rectified and divided into two independent positive directional degrees-of-freedom. The muscle and kinematic synergies were both extracted using the non-negative matrix factorization (NNMF) technique and... 

Phytochemical investigation of the aerial parts of Salvia eremophila led to the isolation of a lupane triterpenoid, 3β, 20-dihydroxylupane-28-oic acid (1), together with eight other compounds, comprising three diterpene, two triterpene, two flavonoids and a steroidal glucoside. Their structures were elucidated by interpretation of their one-dimensional and two-dimensional NMR spectra and completed by the analysis of the HRESIMS data. Compounds 1, 2-4 and 8 were evaluated for their cytotoxicities against five human tumour cell lines. Compounds 1 and 3 hold a good potential for use in future studies due to their anti-cancer properties  

The ultra-supercritical once-through boiler (OTB) unit is an advanced power generation technology with high plant efficiency and low emissions. However, it is difficult to realize a coordinate control for the ultra-supercritical OTB unit to achieve the fast and stable dynamic response during the load tracking and grid frequency disturbances and in the presence of unavoidable uncertainties. In this paper, an accurate grey box multivariable coupled nonlinear model of an ultra-supercritical boiler-turbine unit is considered. Steam pressure at throttle valve, specific enthalpy in separator and active power are adjusted at desired values by manipulation of the fuel rate command, feedwater rate...